A customized non-exclusive clustering algorithm for news recommendation systems
Clustering is one of the main tasks in machine learning and data mining and is being utilized in many applications including news recommendation systems. In this paper, we propose a new non-exclusive clustering algorithm named Ordered Clustering (OC) with the aim is to increase the accuracy of news...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
University of Babylon
2019
|
| Online Access: | http://psasir.upm.edu.my/id/eprint/80406/ http://psasir.upm.edu.my/id/eprint/80406/1/CLUSTER.pdf |
| _version_ | 1848858900392050688 |
|---|---|
| author | Ibrahim, Hamidah Sidi, Fatimah Mustapha, Aida Darvishy, Asghar |
| author_facet | Ibrahim, Hamidah Sidi, Fatimah Mustapha, Aida Darvishy, Asghar |
| author_sort | Ibrahim, Hamidah |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Clustering is one of the main tasks in machine learning and data mining and is being utilized in many applications including news recommendation systems. In this paper, we propose a new non-exclusive clustering algorithm named Ordered Clustering (OC) with the aim is to increase the accuracy of news recommendation for online users. The basis of OC is a new initialization technique that groups news items into clusters based on the highest similarities between news items to accommodate news nature in which a news item can belong to different categories. Hence, in OC, multiple memberships in clusters are allowed. An experiment is carried out using a real dataset which is collected from the news websites. The experimental results demonstrated that the OC outperforms the k-means algorithm with respect to Precision, Recall, and F1-Score. |
| first_indexed | 2025-11-15T12:20:47Z |
| format | Article |
| id | upm-80406 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T12:20:47Z |
| publishDate | 2019 |
| publisher | University of Babylon |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-804062020-11-06T19:03:36Z http://psasir.upm.edu.my/id/eprint/80406/ A customized non-exclusive clustering algorithm for news recommendation systems Ibrahim, Hamidah Sidi, Fatimah Mustapha, Aida Darvishy, Asghar Clustering is one of the main tasks in machine learning and data mining and is being utilized in many applications including news recommendation systems. In this paper, we propose a new non-exclusive clustering algorithm named Ordered Clustering (OC) with the aim is to increase the accuracy of news recommendation for online users. The basis of OC is a new initialization technique that groups news items into clusters based on the highest similarities between news items to accommodate news nature in which a news item can belong to different categories. Hence, in OC, multiple memberships in clusters are allowed. An experiment is carried out using a real dataset which is collected from the news websites. The experimental results demonstrated that the OC outperforms the k-means algorithm with respect to Precision, Recall, and F1-Score. University of Babylon 2019 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/80406/1/CLUSTER.pdf Ibrahim, Hamidah and Sidi, Fatimah and Mustapha, Aida and Darvishy, Asghar (2019) A customized non-exclusive clustering algorithm for news recommendation systems. Journal of University of Babylon, Pure and Applied Sciences (JUBES), 27 (1). pp. 368-379. ISSN 1992-0652; ESSN: 2312-8135 https://www.journalofbabylon.com/index.php/JUBPAS/article/view/2192 10.29196/jubpas.v27i1.2192 |
| spellingShingle | Ibrahim, Hamidah Sidi, Fatimah Mustapha, Aida Darvishy, Asghar A customized non-exclusive clustering algorithm for news recommendation systems |
| title | A customized non-exclusive clustering algorithm for news recommendation systems |
| title_full | A customized non-exclusive clustering algorithm for news recommendation systems |
| title_fullStr | A customized non-exclusive clustering algorithm for news recommendation systems |
| title_full_unstemmed | A customized non-exclusive clustering algorithm for news recommendation systems |
| title_short | A customized non-exclusive clustering algorithm for news recommendation systems |
| title_sort | customized non-exclusive clustering algorithm for news recommendation systems |
| url | http://psasir.upm.edu.my/id/eprint/80406/ http://psasir.upm.edu.my/id/eprint/80406/ http://psasir.upm.edu.my/id/eprint/80406/ http://psasir.upm.edu.my/id/eprint/80406/1/CLUSTER.pdf |